Dynamic Induction of Temporal Interpretation Contexts

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Citation: Yuval Shahar. (1996) Dynamic Induction of Temporal Interpretation Contexts. In KSL-96-01, January,1996.

Publication techreport ( Edit )
type Technical Report
bibtype techreport
Bibtex basics
author Yuval Shahar
title Dynamic Induction of Temporal Interpretation Contexts
number KSL-96-01
institution Knowledge Systems, AI Laboratory
year 1996
month January
Bibtex more
note Medical Computer Science
Access Paper
abstract The temporal-abstraction task is the task of abstracting higher-level concepts from time-stamped data in a context-sensitive manner. We have developed acknowledge-based framework for solving that task. The framework includes a model of time, parameters, events, and interpretation contexts. A formal specification of a domain's temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge. We have defined the knowledge-based temporal-abstraction method, a problem-solving method that decomposes the temporal-abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal-abstraction mechanisms. We present the logical model underlying the representation and runtime formation of interpretation contexts. Interpretation contexts are relevant for abstraction of time-oriented data and are induced dynamically by input data,concluded abstractions, external events, goals of the temporal-abstraction process, and certain combinations of interpretation contexts. Knowledge about interpretation contexts is represented as a context ontology and as a dynamic induction relation over interpretation contexts and other proposition types. Induced interpretation contexts are either basic, composite, generalized, or nonconvex.We discuss several significant advantages of separating explicitly interpretation-context propositions from the propositions inducing them and from the abstractions created within their scope.

KSL Technical Report ID: KSL-96-01
Facts about Dynamic Induction of Temporal Interpretation ContextsRDF feed
Abstract The temporal-abstraction task is the task … The temporal-abstraction task is the task of abstracting higher-level concepts from time-stamped data in a context-sensitive manner. We have developed acknowledge-based framework for solving that task. The framework includes a model of time, parameters, events, and interpretation contexts. A formal specification of a domain's temporal-abstraction knowledge supports acquisition, maintenance, reuse, and sharing of that knowledge. We have defined the knowledge-based temporal-abstraction method, a problem-solving method that decomposes the temporal-abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal-abstraction mechanisms. We present the logical model underlying the representation and runtime formation of interpretation contexts. Interpretation contexts are relevant for abstraction of time-oriented data and are induced dynamically by input data,concluded abstractions, external events, goals of the temporal-abstraction process, and certain combinations of interpretation contexts. Knowledge about interpretation contexts is represented as a context ontology and as a dynamic induction relation over interpretation contexts and other proposition types. Induced interpretation contexts are either basic, composite, generalized, or nonconvex.We discuss several significant advantages of separating explicitly interpretation-context propositions from the propositions inducing them and from the abstractions created within their scope. e abstractions created within their scope.
Author Yuval Shahar  +
Bibtype techreport  +
Has author Yuval Shahar  +
Has identifier KSL-96-01  +
Has publishing details January,1996  +
Has title Dynamic Induction of Temporal Interpretation Contexts  +
Has where published KSL-96-01  +
Has year 1996  +
Institution Knowledge Systems, AI Laboratory  +
Ksl tr id KSL-96-01  +
Month January  +
Note Medical Computer Science
Number KSL-96-01  +
Process note NO  +
Title Dynamic Induction of Temporal Interpretation Contexts  +
Year 1996  +
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